How are physicians delivering palliative care? A population-based retrospective cohort study describing the mix of generalist and specialist palliative care models in the last year of life
Bibliographic record
Abstract
BACKGROUND: To enable coordinated palliative care delivery, all clinicians should have basic palliative care skill sets ('generalist palliative care'). Specialists should have skills for managing complex and difficult cases ('specialist palliative care') and co-exist to support generalists through consultation care and transfer of care. Little information exists about the actual mixes of generalist and specialist palliative care. AIM: To describe the models of physician-based palliative care services delivered to patients in the last 12 months of life. DESIGN: This is a population-based retrospective cohort study using linked health care administrative data. SETTING/PARTICIPANTS: Physicians providing palliative care services to a decedent cohort in Ontario, Canada. The decedent cohort consisted of all adults (18+ years) who died in Ontario, Canada between April 2011 and March 2015 ( n = 361,951). RESULTS: We describe four major models of palliative care services: (1) 53.0% of decedents received no physician-based palliative care, (2) 21.2% received only generalist palliative care, (3) 14.7% received consultation palliative care (i.e. care from both specialists and generalists), and (4) 11.1% received only specialist palliative care. Among physicians providing palliative care ( n = 11,006), 95.3% had a generalist palliative care focus and 4.7% a specialist focus; 74.2% were trained as family physicians. CONCLUSION: We examined how often a coordinated palliative care model is delivered to a large decedent cohort and identified that few actually received consultation care. The majority of care, in both the palliative care generalist and specialist models, was delivered by family physicians. Further research should evaluate how different models of care impact patient outcomes and costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".